The modern communications era has brought about a tremendous expansion of wireline and wireless networks. Computer networks, television networks, and telephony networks are experiencing an unprecedented technological expansion, fueled by consumer demand. Wireless and mobile networking technologies have addressed related consumer demands, while providing more flexibility and immediacy of information transfer.
Current and future networking technologies continue to facilitate ease of information transfer and convenience to users. Due to the now ubiquitous nature of electronic communication devices, people of all ages and education levels are utilizing electronic devices to communicate with other individuals or contacts, receive services and/or share information, media and other content. One area in which there is a demand to increase ease of information transfer relates to context-aware behavior services of communication devices. These context-aware behavior services may be utilized to determine activity recognition such as current activity of a user and/or communication device. Determining with confidence specific aspects of device pose and user motion may provide a foundation for context inference. As such, speed estimation and pose classification of a communication device may be beneficial for activity recognition. For example, speed estimation and pose classification information may be utilized by a communication device to determine that a person walking briskly with the communication device in a pocket is likely not also drinking a cup of coffee.
By determining a user's walking speed based on speed estimation information, a route-finding application of a mobile device may better estimate location. Additionally, pose classifications of a mobile device may be exposed to applications of the mobile device allowing the applications to determine whether certain features should be computed or displayed. For example, an application may reduce its energy consumption by deferring activity that would only be relevant if the user were looking at the mobile device while it was in the user's hand. As people expect more context-aware behavior from their mobile devices, seamlessly inferring their current activity has emerged as a relevant challenge.
Due to recent advances in sensor technology, many current mobile devices are equipped with sensors that may be used to capture user motion data and infer user activities and context. However, existing solutions relating to user context recognition using acceleration typically assumes that one or more sensors are attached to known positions on the body of the user (e.g., a chest or hip), or assumes that the mobile device, such as a mobile phone, was in a known, fixed position (e.g., a pocket).
However, these assumptions may not match normal users' typical usage patterns such as, for example, people carrying their mobile phone in different places at different times, sometimes rapidly changing its position.
In view of the foregoing drawbacks, it may be beneficial to provide an efficient and reliable mechanism of determining speed of movement of a device and device pose classifications.